This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared
task on Multilingual Parsing from Raw Text to Universal Dependencies. We base
our submission on Stanford's winning system for the CoNLL 2017 shared task and
make two effective extensions: 1) incorporating deep contextualized word
embeddings into both the part of speech tagger and parser; 2) ensembling
parsers trained with different initialization. We also explore different ways
of concatenating treebanks for further improvements. Experimental results on
the development data show the effectiveness of our methods. In the final
evaluation, our system was ranked first according to LAS (75.84%) and
outperformed the other systems by a large margin.

本文描述了我们的系统（HIT-SCIR），该系统提交给 CoNLL 2018 共享任务，涉及从原始文本到通用依赖的多语言解析。我们基于斯坦福的获胜系统进行提交和进行了两个有效的扩展：1）将深度上下文化词嵌入到词性标记器和解析器中；2）集合使用不同初始化的解析器进行训练。我们还探索了不同的合并树库的方法以进行进一步的改进。开发数据上的实验结果显示了我们方法的有效性。在最后的评估中，我们的系统在 LAS（75.84％）上排名第一，并大幅跑赢了其他系统。